Обзор подходов и практических областей применения распознавания видов физической активности человека
Автор: Тарантова Елена Сергеевна, Макаров Кирилл Владимирович, Орлов Алексей Александрович
Статья в выпуске: 3 т.8, 2019 года.
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Распознавание видов физической активности человека является одним из актуальных направлений исследования в области машинного обучения, так как результаты распознавания необходимы при решении многих практических задач. В статье приводится обзор подходов и практических областей применения методов распознавания видов физической активности человека. Рассматриваются датчики, используемые для распознавания видов физической активности человека, и представлены критерии их выбора. Представлены возможные пути решения проблемы выбора места размещения и ориентации носимых датчиков. В статье рассматриваются основные этапы распознавания видов физической активности человека. Представлены извлекаемые признаки и методы их отбора для повышения точности классификации видов физической активности человека и снижения вычислительных затрат за счет уменьшения числа признаков. Сформулированы достоинства и недостатки популярных методов классификации. Рассматриваются метрики, используемые для оценки качества обучения модели классификации. Наиболее применяемой метрикой качества является кривая ошибок. Также представлены практические задачи, в которых необходимы результаты распознавания видов физической активности человека. Основными областями применения метода распознавания являются медицина, производство, фитнес и безопасность людей. В заключении представлены возможные направления будущих исследований.
Распознавание образов, машинное обучение, виды физической активности человека
Короткий адрес: https://sciup.org/147233200
IDR: 147233200 | УДК: 004.93'1 | DOI: 10.14529/cmse190303
Survey on approaches and practical areas of human activity recognition application
Human activity recognition is one of the relevant fields of research in machine learning since recognition results are necessary for solving many practical problems. The article provides a survey on approaches and areas of practical application of methods for human activity recognition. The sensors used for human activity recognition are considered, and the criteria for their selection are presented. Possible solutions to the problem of choosing the location and orientation of wearable sensors are presented. The main stages of human activity recognition are discussed in the article. Extracted features and methods of their selection to increase the accurate classification of human activity recognition and reduce computational complexity by cutting down the number of features are presented. The advantages and disadvantages of popular classification methods are formulated. The metrics used to evaluate the quality of learning classification models are considered. The most commonly used quality metric is the error curve. Practical tasks in which the results of human activity recognition are needed are also presented. The main areas of human activity recognition application are medicine, manufacturing, fitness, and safety of people. In conclusion, possible future research directions are presented.
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